高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

接收域分离的跨接收系统通用性辐射源指纹识别

孙丽婷 柳征 黄知涛

孙丽婷, 柳征, 黄知涛. 接收域分离的跨接收系统通用性辐射源指纹识别[J]. 电子与信息学报. doi: 10.11999/JEIT240171
引用本文: 孙丽婷, 柳征, 黄知涛. 接收域分离的跨接收系统通用性辐射源指纹识别[J]. 电子与信息学报. doi: 10.11999/JEIT240171
SUN Liting, LIU Zheng, HUANG Zhitao. Universal Radio Frequency Fingerprinting across Receiving Systems Using Receiving Domain Separation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240171
Citation: SUN Liting, LIU Zheng, HUANG Zhitao. Universal Radio Frequency Fingerprinting across Receiving Systems Using Receiving Domain Separation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240171

接收域分离的跨接收系统通用性辐射源指纹识别

doi: 10.11999/JEIT240171
基金项目: 国家自然科学基金(62301575),国防科技大学青年自主创新科学基金项目(ZK2023-19)
详细信息
    作者简介:

    孙丽婷:女,讲师,研究方向为信号处理、辐射源个体识别

    柳征:男,研究员,研究方向为雷达信号处理、电子对抗

    黄知涛:男,教授,研究方向为认知电子战、电子对抗

    通讯作者:

    孙丽婷 slt2009@yeah.net

  • 中图分类号: TN97

Universal Radio Frequency Fingerprinting across Receiving Systems Using Receiving Domain Separation

Funds: The National Natural Science Foundation of China (62301575), The Youth Independent Innovation Science Fund Project of National University of Defense Technology (ZK2023-19)
  • 摘要: 受辐射源硬件失真和接收机硬件失真的耦合作用,实际接收信号中带有当前辐射源系统和接收系统共同的“个体信息”,导致辐射源指纹识别技术(RFF)在跨接收系统场景下无法通用。为消除接收机染色效应,该文将接收机影响作为单独作用域,提出了一种基于接收域分离的跨接收系统通用性辐射源指纹识别方法。该方法通过双标签多通道特征联合和域分离对抗重构方式实现信号中辐射源指纹作用域与接收机染色作用域分离,利用多部接收机数据预先训练网络对两种作用域的分离能力,聚焦辐射源指纹信息提取,从而提升辐射源指纹识别技术在跨平台跨接收系统、更新接收设备等场景下的适应能力。相比于直接特征提取和多接收机打包训练方式,所提方法能够真正适应实际无监督场景,且参与训练的源域接收机数目越多,域适应效果越好,不需要重复训练即可直接推广应用于新接收系统,具有较高的实际应用价值。
  • 图  1  典型发射机和接收机硬件结构

    图  2  跨接收系统场景下算法示意图

    图  3  基于接收域分离的跨接收系统通用性辐射源指纹识别算法实现框架

    图  4  域特征提取器结构

    图  5  解码器结构

    图  6  单接收机直接训练方法跨域适应能力

    图  7  发射机作用域特征降维后分布图

    图  8  接收机作用域特征降维后分布图

    图  9  新接收机适应能力测试

    表  1  辐射源失真参数设置

    标签 滤波器失真 I/Q不平衡 功率放大器 杂散单音与载频泄露
    $({a_0},{a_1},{\alpha _1})$ $({b_0},{b_1},{\beta _1})$ $G$ $\tau $ $({a_1},{a_2},{a_3})$ ${a_{{\text{ST}}}}$ ${f_{{\text{ST}}}}$ $\xi ({10^{ - 3}})$
    E1 (1, 0.030, 0.25) (1, 0.030 2, 0.25) 0.999 8 –0.018 (1, 0.50, 0.30) 0.008 2 0.012 9 1.3+8.2j
    E2 (1, 0.060, 0.25) (1, 0.029 5, 0.25) 1.005 6 0.0175 (1, 0.08, 0.60) 0.007 5 0.013 2 1.5+7.2j
    E3 (1, 0.085, 0.25) (1, 0.029 0, 0.25) 1.010 2 0.012 (1, 0.01, 0.01) 0.007 0 0.012 3 1.1+6.8j
    E4 (1, 0.073, 0.25) (1, 0.031 0, 0.25) 0.999 2 0.003 (1, 0.01, 0.40) 0.008 7 0.013 5 1.7+9.0j
    E5 (1, 0.040, 0.25) (1, 0.031 3, 0.25) 0.998 2 0.024 (1, 0.60, 0.08) 0.00 90 0.011 9 2.0+6.5j
    下载: 导出CSV

    表  2  接收机失真参数设置

    标签随机性失真确定性失真
    相位噪声采样抖动量化噪声滤波器失真低噪声放大器失真
    ($\sigma _\theta ^2$,${\varOmega _0}$) $v$ $\beta $ $({a_0},{a_1},{T_{\text{a}}})$ $({b_0},{b_1},{T_{\text{b}}})$ $({c_1},{c_2},{c_3})$
    R1(1,0.001)0.0201(1,0.010,4)(1,0.031 5,4)(1,0.10,0.01)
    R2(1,0.010)0.0232(1,0.015,4)(1,0.030 5,4)(1,0.15,0.02)
    R3(1,0.020)0.0303(1,0.020,4)(1,0.029 5,4)(1,0.20,0.03)
    R4(1,0.021)0.0404(1,0.025,4)(1,0.028 5,4)(1,0.25,0.04)
    R5(1,0.022)0.0455(1,0.030,4)(1,0.027 5,4)(1,0.30,0.05)
    R6(1,0.023)0.0506(1,0.035,4)(1,0.026 5,4)(1,0.35,0.06)
    R7(1,0.024)0.0607(1,0.040,4)(1,0.025 5,4)(1,0.40,0.07)
    R8(1,0.025)0.0703(1,0.045,4)(1,0.024 5,4)(1,0.45,0.08)
    R9(1,0.026)0.0733(1,0.050,4)(1,0.023 5,4)(1,0.50,0.09)
    R10(1,0.027)0.0803(1,0.055,4)(1,0.022 5,4)(1,0.55,0.10)
    下载: 导出CSV

    表  3  单接收机直接训练方法的正确识别率(%)

    R1R2R3R4R5R6R7R8R9R10
    正确识别率100.00100.0099.50100.0099.5098.0098.5098.50100.0097.50
    下载: 导出CSV

    表  4  单接收机直接训练方法的跨域适应能力统计(%)

    目标域R1R2R3R4R5R6R7R8R9R10均值
    平均识别率43.0049.5054.5055.9456.6755.5051.4448.8346.2839.0050.07
    源域R1R2R3R4R5R6R7R8R9R10均值
    平均识别率40.3945.6748.1154.1157.1153.1160.2250.5052.4439.0050.07
    下载: 导出CSV

    表  5  多接收机打包统一训练方法的正确识别率(含跨域适应)(%)

    R1R2R3R4R5R6R7R8R9R10均值
    R1100.0098.5062.0051.0038.0021.0020.0020.0020.0020.0038.94
    R12100.00100.0099.5089.5066.5047.0029.0021.0020.0020.0049.06
    R123100.00100.00100.00100.00100.0084.0064.0040.0022.5020.0061.50
    R1234100.00100.00100.00100.00100.0099.5092.5074.0044.5023.0072.25
    R12345100.00100.00100.00100.00100.00100.00100.0093.5077.0044.5083.00
    均值100.0099.7092.3088.1080.9070.3061.1049.7036.8025.50-
    下载: 导出CSV

    表  6  接收域分离方法的正确识别率(%)

    R1R2R3R4R5R6R7R8R9R10均值
    R1100.0099.4889.5844.7955.2144.2737.5030.7338.5420.8351.22
    R1299.4899.4895.3189.0668.7571.8853.1356.2536.9841.1564.06
    R123100.0099.4899.48100.0093.2396.8891.1585.9479.1748.9685.05
    R1234100.00100.0096.35100.00100.0099.4897.9293.7590.1073.4492.45
    R12345100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00
    均值99.9099.6996.1486.7783.4482.5075.9473.3368.9656.88-
    下载: 导出CSV

    表  7  多接收域分离方法消融实验的正确识别率(%)

    损失函数网络组成识别率损失函数网络组成识别率
    ${\mathcal{L}_{{\text{2 }}}} + {\mathcal{L}_{{\text{3 }}}}$RF1+RF2+EF+RC91.67${\mathcal{L}_{{\text{1 }}}} + {\mathcal{L}_{{\text{2 }}}}$RF1+RF2+EF98.44
    ${\mathcal{L}_{{\text{2 }}}} + {\mathcal{L}_{{\text{4 }}}}$RF1+RF2+EF+DE99.48${\mathcal{L}_{{\text{2 }}}} + {\mathcal{L}_{{\text{3 }}}}$RF1+EF+RC+DE95.31
    ${\mathcal{L}_{{\text{1 }}}} + {\mathcal{L}_{{\text{2 }}}}$RF1+RF2+EF+DE98.50${\mathcal{L}_{{\text{2 }}}}$EF44.50
    无GRLRF1+RF2+EF+RC+DE94.27等权重RF1+RF2+EF+RC+DE93.23
    下载: 导出CSV
  • [1] XU Zhengwei, HAN Guangjie, LI Liu, et al. A lightweight specific emitter identification model for IIoT devices based on adaptive broad learning[J]. IEEE Transactions on Industrial Informatics, 2023, 19(5): 7066–7075. doi: 10.1109/TII.2022.3206309.
    [2] ZHA Haoran, WANG Hanhong, FENG Zhongming, et al. LT-SEI: Long-tailed specific emitter identification based on decoupled representation learning in low-resource scenarios[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 52(1): 929–943. doi: 10.1109/TITS.2023.3308716.
    [3] 韦建宇, 俞璐. 通信辐射源个体识别中的特征提取方法综述[J]. 通信技术, 2022, 55(6): 681–687. doi: 10.3969/j.issn.1002-0802.2022.06.001.

    WEI Jianyu and YU Lu. Overview of radio frequency fingerprint extraction in communication specific emitter identification[J]. Communications Technology, 2022, 55(6): 681–687. doi: 10.3969/j.issn.1002-0802.2022.06.001.
    [4] HE Boxiang and WANG Fanggang. Specific emitter identification via sparse Bayesian learning versus model-agnostic meta-learning[J]. IEEE Transactions on Information Forensics and Security, 2023, 18: 3677–3691. doi: 10.1109/TIFS.2023.3287073.
    [5] REHMAN S U, SOWERBY K W, and COGHILL C. Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers[J]. Journal of Computer and System Sciences, 2014, 80(3): 591–601. doi: 10.1016/j.jcss.2013.06.013.
    [6] RAMSEY B W, STUBBS T D, MULLINS B E, et al. Wireless infrastructure protection using low-cost radio frequency fingerprinting receivers[J]. International Journal of Critical Infrastructure Protection, 2015, 8: 27–39. doi: 10.1016/j.ijcip.2014.11.002.
    [7] 乐波, 王桂良, 黄渊凌, 等. 接收机畸变对辐射源指纹识别的影响[J]. 电讯技术, 2020, 60(3): 273–278. doi: 10.3969/j.issn.1001-893x.2020.03.005.

    LE Bo, WANG Guiliang, HUANG Yuanling, et al. Influence of receiver distortion characteristics on specific emitter identification[J]. Telecommunication Engineering, 2020, 60(3): 273–278. doi: 10.3969/j.issn.1001-893x.2020.03.005.
    [8] . ZHENG Yenan, YING Wenwei, HONG Shaohua, et al. A method for cross-receiver specific emitter identification based on CBAM-CNN-BDA[C]. 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Dali, China, 2022: 1320–1324. doi: 10.1109/ICCASIT55263.2022.9987240.
    [9] FANG Yuyuan, WEI Song, ZHAO Yang, et al. Radar-specific emitter identification with only envelope power based on multidimensional complex noncentral chi-square classifier[J]. IEEE Sensors Journal, 2023, 23(17): 20223–20235. doi: 10.1109/JSEN.2023.3298352.
    [10] TAN Kaiwen, YAN Wenjun, ZHANG Limin, et al. Semi-supervised specific emitter identification based on bispectrum feature extraction CGAN in multiple communication scenarios[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(1): 292–310. doi: 10.1109/TAES.2022.3184619.
    [11] ZHU Chenyu, LIU Liang, and PENG Xiaoyan. Specific emitter identification based on temporal convolutional network sequence processing[J]. IEEE Communications Letters, 2023, 27(10): 2667–2671. doi: 10.1109/LCOMM.2023.3312390.
    [12] WU Zitao, WANG Fanggang, and HE Boxiang. Specific emitter identification via contrastive learning[J]. IEEE Communications Letters, 2023, 27(4): 1160–1164. doi: 10.1109/LCOMM.2023.3247900.
    [13] . BALDINI G, GIULIANI R, GENTILE C, et al. Measures to address the lack of portability of the RF fingerprints for radiometric identification[C]. 9th IFIP International Conference on New Technologies, Mobility and Security, Paris, France, 2018: 1–5. doi: 10.1109/NTMS.2018.8328703.
    [14] SHI Mengkai, HUANG Yuanling, and WANG Guiliang. Carrier leakage estimation method for cross-receiver specific emitter identification[J]. IEEE Access, 2021, 9: 26301–26312. doi: 10.1109/ACCESS.2021.3058167.
    [15] HE Boxiang and WANG Fanggang. Cooperative specific emitter identification via multiple distorted receivers[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 3791–3806. doi: 10.1109/TIFS.2020.30017210.
    [16] . FAWAZ H I, DEL GROSSO G, KERDONCUFF T, et al. Deep unsupervised domain adaptation for time series classification: A benchmark[EB/OL]. https://arXiv.org/abs/2312.09857v2, 2023.
    [17] . LI Ya, GONG Mingming, TIAN Xinmei, et al. Domain generalization via conditional invariant representations[C]. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 3579–3587. doi: 10.1609/aaai.v32i1.11682.
    [18] . MATSUURA T and HARADA T. Domain generalization using a mixture of multiple latent domains[C]. Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020: 11749–11756. doi: 10.1609/aaai.v34i07.6846.
    [19] GANIN Y, USTINOVA E, AJAKAN H, et al. Domain-adversarial training of neural networks[J]. The Journal of Machine Learning Research, 2016, 17(1): 2096–2030.
    [20] BOUSMALIS K, TRIGEORGIS G, SILBERMAN N, et al. Domain separation networks[C]. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 343–351.
    [21] SUN Liting, WANG Xiang, HUANG Zhitao, et al. Radio-frequency fingerprint extraction based on feature inhomogeneity[J]. IEEE Internet of Things Journal, 2022, 9(18): 17292–17308. doi: 10.1109/JIOT.2022.3154595.
    [22] SRIDHARAN G. Phase noise in multi-carrier systems[D]. [Master dissertation], University of Toronto, 2010.
    [23] HUANG Yuanling and ZHENG Hui. Theoretical performance analysis of radio frequency fingerprinting under receiver distortions[J]. Wireless Communications and Mobile Computing, 2015, 15(5): 823–833. doi: 10.1002/wcm.2386.
    [24] 陈翔, 汪连栋, 许雄, 等. 基于Raw I/Q和深度学习的射频指纹识别方法综述[J]. 雷达学报, 2023, 12(1): 214–234. doi: 10.12000/JR22140.

    CHEN Xiang, WANG Liandong, XU Xiong, et al. A review of radio frequency fingerprinting methods based on Raw I/Q and deep learning[J]. Journal of Radars, 2023, 12(1): 214–234. doi: 10.12000/JR22140.
    [25] . 郭瑞鹏. 基于深度学习的雷达辐射源个体识别技术研究[D]. [硕士论文], 西安电子科技大学, 2022. doi: 10.27389/d.cnki.gxadu.2022.002199.

    GUO Ruipeng. Research on individual identification technology of radar emitter based on deep learning[D]. [Master dissertation], Xidian University, 2022. doi: 10.27389/d.cnki.gxadu.2022.002199.
    [26] DING Lida, WANG Shilian, WANG Fanggang, et al. Specific emitter identification via convolutional neural networks[J]. IEEE Communications Letters, 2018, 22(12): 2591–2594. doi: 10.1109/LCOMM.2018.2871465.
    [27] AL-SHAWABKA A, RESTUCCIA F, D’ORO S, et al. Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting[C]. IEEE Conference on Computer Communications, Toronto, Canada, 2020: 646–655. doi: 10.1109/INFOCOM41043.2020.9155259.
  • 加载中
图(9) / 表(7)
计量
  • 文章访问数:  84
  • HTML全文浏览量:  34
  • PDF下载量:  11
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-03-14
  • 修回日期:  2024-07-15
  • 网络出版日期:  2024-07-22

目录

    /

    返回文章
    返回